Evidence map›Paper›PMID 33432920›Full record

ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2021

Identifying predictive factors for sleep bruxism severity using clinical and polysomnographic parameters: a principal component analysis.

João Vicente Rosar, Maria Carolina Salomé Marquezin, Aianne Souto Pizzolato, Fernanda Yukie Kobayashi, Sandra Kalil Bussadori, Luciano José Pereira, Paula Midori Castelo

Open access · greenAbstract read
In one paragraph

Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.5field-weighted citation impact, top 21% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 1 country.

João Vicente RosarDepartment of Orthodontics, Universidade de Chapecó, Chapecó, Brazil.
Maria Carolina Salomé MarquezinDepartment of Pharmaceutical Sciences, Universidade Federal de São Paulo, Diadema, Brazil.
Aianne Souto PizzolatoDepartment of Pharmaceutical Sciences, Universidade Federal de São Paulo, Diadema, Brazil.
Fernanda Yukie KobayashiDepartment of Rehabilitation Sciences, Universidade Nove de Julho, São Paulo, Brazil.
Sandra Kalil BussadoriDepartment of Rehabilitation Sciences, Universidade Nove de Julho, São Paulo, Brazil.
Luciano José PereiraDepartment of Health Sciences, Universidade Federal de Lavras, Lavras, Brazil.
Paula Midori CasteloDepartment of Pharmaceutical Sciences, Universidade Federal de São Paulo, Diadema, Brazil.
Universidade Federal de São Paulo · BRUniversidade Nove de Julho · BRCommunity University of Chapecó Region - Unochapecó · BRUniversidade Federal de Lavras · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

STUDY

objectivesThe aim was to identify predictive factors for sleep bruxism (SB) severity among polysomnographic parameters, salivary cortisol levels, temporomandibular disorders, age, and sex.

methodsYoung adults (19-30 years) were screened for self-/roommate reports of teeth grinding/clenching during sleep associated with clinical signs of tooth wear. Individuals positive for both conditions were administered a polysomnographic exam to provide a definite diagnosis of SB (n = 28). Healthy participants without SB signs/symptoms were also included (n = 15). The Research Diagnostic Criteria for Temporomandibular Disorders was applied to determine functional, muscular, and articular domains of the Temporomandibular Index. Cortisol awakening levels were measured in saliva. Principal component analysis was used to extract the latent components emerging from polysomnographic results, and 2 regression models were adjusted to predict the number and duration of bruxism episodes.

resultsPrincipal component analysis resulted in 4 components-C1: %N1, total sleep time, sleep efficiency, arousals/microarousals; C2: %N2, %N3; C3: periodic limb movements and apneas; C4: %REM and REM latency. The number of SB episodes/h was predicted by increasing muscular scores and C2 (decrease in %N2 and increase in %N3) (adjusted R² = 45%; P =.001). The total time of SB episodes was predicted by decreased articular and increased functional scores, age, and female sex (adjusted R² = 36%; P = 0.010). Salivary cortisol levels were not associated with SB severity and did not differ between groups.

conclusionsThe findings showed that SB severity was predicted by muscular and functional scores, female sex, and distinct polysomnographic patterns, contributing to the deeper knowledge of the underlying pathophysiology of SB severity; additionally, the findings can help to formulate health approaches that are specific to the patient and will better assist in treating this condition.

Indexed as

Sleep BruxismFemaleHumansPolysomnographyPrincipal Component AnalysisSalivaSleepYoung Adultbruxismsleep disturbancetemporomandibular disorders

Identifiers

PMID33432920
PMCPMC8320507
OpenAlexW3118680143

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.